{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 利用linesapce函数生成等差数列：0至100的1000个数字\n",
    "x = np.linspace(0,100,1000)\n",
    "\n",
    "# 定义截距项为-1，斜率为2\n",
    "intercept = -1\n",
    "slope = 2\n",
    "\n",
    "# 生成一组正态分布的随机数：均值为0，方差为10，生成1000个噪音\n",
    "e = np.random.normal(0, 10, 1000)\n",
    "\n",
    "# 输入线性回归公式\n",
    "y = intercept + x*slope + e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2b0de1e3d00>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 调用线性回归，绘制图形，查看拟合情况\n",
    "\n",
    "model = LinearRegression()\n",
    "\n",
    "# 创建一个改变了尺寸的新数组\n",
    "x_reshape = x.reshape(-1,1)\n",
    "y_reshape = y.reshape(-1,1)\n",
    "model.fit(x_reshape, y_reshape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.99348328]]\n",
      "[-0.20321406]\n"
     ]
    }
   ],
   "source": [
    "print(model.coef_)\n",
    "print(model.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict = model.predict(x_reshape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2b0daf50af0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x,y)\n",
    "plt.scatter(x,y_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
